Monday, June 9, 2014

Analytics Maturity

See the book site for "High-Performance Data Mining and Big Data Analytics: The Story of Insight from Big Data" (http://bigdataminingbook.info ).

Like many new technology terms in the last two decades, "analytics" has been used and abused in different contexts. Depending on the background, data professionals have a different view of what analytics is. If one comes from IT, database, reporting, or business analysis backgrounds, he/she will consider any manipulation of data that generates reports, aggregated results, or data slicing and dicing as analytics. If one comes from the data mining, machine learning, or statistical modeling experiences, he/she will only consider analytics as where sophisticated algorithms are applied to the data.

The good news is that this battle has already been fought and settled. Today, there is a clear distinction between these two interpretations of Analytics. The former is called basic analytics (low end or spreadsheet-kind of analysis) often referred to as looking at the behind mirror. The latter is referred to as advanced analytics (high end) where the goal is to use sophisticated techniques on the past data to make predictions in the future. With big data, even basic analytics like simple aggregations and tabulation of the data for reporting becomes a challenging task if response time is at all of concern. Those who focus on basic analytics tasks are like journalists while those who focus on advanced analytics may be called innovators.Both types of analytics are essential to the well- being of a business.

The fundamental principle is that an organization cannot transition into advanced analytics era if they have not already mastered the basic analytics applications. In other words, basic analytics is the requirement before entering into advanced analytics, and both are dependent on solid data management infrastructures. This so-called analytics maturity is discussed at length in my book in different contexts (including big data context) and assessing it is necessary prior to any effort to augment a firm's analytics capabilities.